{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 共享单车数据探索 \n",
    "数据集来自Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）。 共731个数据点，涵盖了共享单车13种特征和非注册用户个数casual, 注册用户个数registered, 给定日期（天）总租车人数cnt，三个y标签。训练数据为2011年的数据，要求预测2012年每天的单车共享数量。原始数据集为csv格式, 使用pandas做数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# path to where the data lies\n",
    "#dpath = './data/'\n",
    "data = pd.read_csv(\"day.csv\")  # return DataFrame\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 查看是否有空值\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 探索数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看数据各特征的分布，以及特征之间是否存在相关关系等冗余。\n",
    "\n",
    "我们可以借用可视化工具来直观感觉数据的分布。\n",
    "\n",
    "在Python中，有很多数据可视化途径。\n",
    "Matplotlib非常强大，也很复杂，不易于学习。 \n",
    "Seaborn是在matplotlib的基础上进行了更高级的API封装，从而使得作图更加容易，在大多数情况下使用seaborn就能做出很具有吸引力的图，而使用matplotlib就能制作具有更多特色的图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 各属性的统计特性\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此处得到各属性的样本数目、均值、标准差、最小值、1/4分位数（25%）、中位数（50%）、3/4分位数（75%）、最大值\n",
    "可初步了解各特征的分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 单变量分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python36\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标y（给定日期（天）总租车人数cnt）的直方图／分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values, bins=30, kde=True)\n",
    "plt.xlabel('count of total rental bikes including both casual and registered', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(data.shape[0]), data[\"cnt\"].values,color='purple')\n",
    "plt.title(\"Distribution of cnt\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，数据大多集中在均值（4504）附近，和正态分布比较接近。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 输入属性的直方图／分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.season, order=[1, 2, 3, 4]);\n",
    "plt.xlabel('Season(1:springer, 2:summer, 3:fall, 4:winter)');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.yr, order=[0, 1]);\n",
    "plt.xlabel('year (0: 2011, 1:2012)')\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.mnth, order=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);\n",
    "plt.xlabel('Month ( 1 to 12)');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.holiday, order=[0, 1]);\n",
    "plt.xlabel('holiday (0: no, 1:yes)');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.weekday, order=[0, 1, 2, 3, 4, 5, 6]);\n",
    "plt.xlabel('weekday (day of the week)');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.workingday, order=[0, 1]);\n",
    "plt.xlabel('workingday (1=workingday 0=weekend)(if day is neither weekend nor holiday is 1, otherwise is 0.)');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.weathersit, order=[1, 2, 3, 4]);\n",
    "plt.xlabel('weather condition (1:sunny 2:foggy 3:light snow/light rain 4:heavy snow/heavy rain)');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python36\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbsAAAENCAYAAACW+st2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHJZJREFUeJzt3Xm8JGV56PHfwyaIINtAQBhGIxrRq2AmgDsKKiAIRlBEFBQc8Ro1V4yARi+iXlETvebqvTiiEVEEglEIwbgg4y44hEUBEYRhkW1Yht0o+tw/3vcwNU13nz7D9Onu4vf9fM7n1N5PvV1VT71V1W9FZiJJUputNuoAJEkaNpOdJKn1THaSpNYz2UmSWs9kJ0lqPZOdJKn1THaSpNZbY9QBdBMRS4C7gT8CD2Tm/IjYCDgFmAcsAV6VmXeMKkZJ0uQY55rdCzNzu8ycX/uPBM7OzG2As2u/JEnTinFsQaXW7OZn5q2NYZcDO2fmjRGxObAoM5/caxmbbLJJzps3b+ixSlKbnH/++bdm5pxRx7GqjeVlTCCBb0dEAp/NzIXAZpl5I0BNeJv2W8C8efNYvHjxLIQqSe0REdeMOoZhGNdk95zMvKEmtO9ExK8GmSkiFgALAObOnTvM+CRJE2Qs79ll5g31/y3A14EdgJvr5Uvq/1u6zLcwM+dn5vw5c1pXC5ckraSxS3YRsW5ErDfVDbwE+CVwBnBQnewg4PTRRChJmjTjeBlzM+DrEQElvpMy8z8i4ufAqRFxCHAtsN8IY5QkTZCxS3aZeRXwjC7DbwN2mf2IJEmTbuwuY0qStKqZ7CRJrWeykyS1nslOktR6Y/eAiiStSiede+1A0x2wow1RtJk1O0lS65nsJEmtZ7KTJLWeyU6S1HomO0lS65nsJEmtZ7KTJLWeyU6S1HomO0lS65nsJEmtZ7KTJLWeyU6S1HomO0lS65nsJEmtZ7KTJLWeyU6S1HomO0lS65nsJEmtZ7KTJLWeyU6S1HomO0lS65nsJEmtZ7KTJLWeyU6S1HomO0lS65nsJEmtZ7KTJLWeyU6S1HomO0lS660x6gDUXiede+1A0x2w49whRyLpkW4sa3YRsXpEXBARZ9b+x0fEuRFxRUScEhFrjTpGSdLkGMtkB7wDuKzR/1Hgk5m5DXAHcMhIopIkTaSxS3YRsSXwMuD42h/Ai4DT6iQnAPuMJjpJ0iQau2QH/G/g3cCfav/GwLLMfKD2Xw88rtuMEbEgIhZHxOKlS5cOP1JJ0kQYq2QXEXsCt2Tm+c3BXSbNbvNn5sLMnJ+Z8+fMmTOUGCVJk2fcnsZ8DvDyiNgDWBtYn1LT2yAi1qi1uy2BG0YYoyRpwoxVzS4zj8rMLTNzHrA/8L3MfC1wDrBvnewg4PQRhShJmkBjlez6OAJ4Z0RcSbmH9/kRxyNJmiDjdhnzQZm5CFhUu68CdhhlPJKkyTUpNTtJklaayU6S1HomO0lS643tPTupkw1LPzL4PWsYrNlJklrPZCdJaj2TnSSp9bxnp5Eb9B6NJK0sa3aSpNYz2UmSWs9kJ0lqPe/ZSY8gw/gNm7+L0ySwZidJaj2TnSSp9Ux2kqTWM9lJklrPZCdJaj2TnSSp9Ux2kqTWM9lJklrPH5VLq5A/sJ49NiCumbBmJ0lqPZOdJKn1THaSpNYz2UmSWs9kJ0lqPZOdJKn1THaSpNYz2UmSWs9kJ0lqPZOdJKn1THaSpNYz2UmSWs+GoKUBjHujw+MenzRqY1ezi4i1I+K8iLgoIi6JiA/U4Y+PiHMj4oqIOCUi1hp1rJKkyTB2yQ74L+BFmfkMYDtgt4jYCfgo8MnM3Aa4AzhkhDFKkibI2CW7LO6pvWvWvwReBJxWh58A7DOC8CRJE2gs79lFxOrA+cATgc8AvwGWZeYDdZLrgcd1mW8BsABg7lxfjjks3h9SG/ni3XYbu5odQGb+MTO3A7YEdgCe0m2yLvMtzMz5mTl/zpw5ww5TkjQhxjLZTcnMZcAiYCdgg4iYqoluCdwwqrgkSZNl7JJdRMyJiA1q9zrArsBlwDnAvnWyg4DTRxOhJGnSjOM9u82BE+p9u9WAUzPzzIi4FDg5Ij4EXAB8fpRBSpImx9glu8y8GNi+y/CrKPfvpL580GA8+WCTRmnsLmNKkrSqmewkSa1nspMktd7Y3bOTZsso7yF5X1GaXdbsJEmtZ7KTJLWeyU6S1HomO0lS65nsJEmtZ7KTJLWeyU6S1HomO0lS65nsJEmtZ7KTJLWeyU6S1HomO0lS69kQtCTNwEwaELch7/FhzU6S1HomO0lS65nsJEmtZ7KTJLWeyU6S1HomO0lS65nsJEmtZ7KTJLWeyU6S1HomO0lS65nsJEmtZ7KTJLWeyU6S1HomO0lS65nsJEmtZ7KTJLWeL2+VxthMXhTahs+VhmXsanYRsVVEnBMRl0XEJRHxjjp8o4j4TkRcUf9vOOpYJUmTYeySHfAAcHhmPgXYCXhrRGwLHAmcnZnbAGfXfkmSpjV2yS4zb8zM/6zddwOXAY8D9gZOqJOdAOwzmgglSZNm7JJdU0TMA7YHzgU2y8wboSREYNPRRSZJmiRjm+wi4jHA14C/zcy7BpxnQUQsjojFS5cuHW6AkqSJMZbJLiLWpCS6r2Tmv9bBN0fE5nX85sAtnfNl5sLMnJ+Z8+fMmTN7AUuSxtrYJbuICODzwGWZ+YnGqDOAg2r3QcDpsx2bJGkyjePv7J4DvA74RURcWIe9BzgWODUiDgGuBfYbUXySpAkzdskuM38ERI/Ru8xmLI80/pBYUluN3WVMSZJWNZOdJKn1THaSpNYz2UmSWs9kJ0lqPZOdJKn1THaSpNYz2UmSWs9kJ0lqPZOdJKn1THaSpNYz2UmSWs9kJ0lqPZOdJKn1THaSpNYz2UmSWm/sXt6qVcsXskqSNTtJ0iOAyU6S1HomO0lS63nPbkJ5L06SBmfNTpLUeiY7SVLrmewkSa1nspMktZ7JTpLUeiY7SVLrmewkSa1nspMktZ7JTpLUeiY7SVLrmewkSa1nspMktZ4NQUvSkAzaYPsBO84dciQau5pdRHwhIm6JiF82hm0UEd+JiCvq/w1HGaMkabKMXbIDvgjs1jHsSODszNwGOLv2S5I0kLFLdpn5A+D2jsF7AyfU7hOAfWY1KEnSRBu7ZNfDZpl5I0D9v+mI45EkTZBJSXYDiYgFEbE4IhYvXbp01OFIksbEpCS7myNic4D6/5ZuE2Xmwsycn5nz58yZM6sBSpLG16QkuzOAg2r3QcDpI4xFkjRhxi7ZRcRXgZ8CT46I6yPiEOBY4MURcQXw4tovSdJAxu5H5Zn5mh6jdpnVQCRplvjj8+Ebu5qdJEmrmslOktR6JjtJUuuZ7CRJrWeykyS1nslOktR6JjtJUuuZ7CRJrTd2Pyp/pBv0x6WSpMFZs5MktZ7JTpLUeiY7SVLrmewkSa1nspMktZ7JTpLUeiY7SVLrmewkSa1nspMktZ7JTpLUeiY7SVLrmewkSa1nQ9CSNCEGbSj+gB3nDjmSyWPNTpLUeiY7SVLrmewkSa3nPbuHyZetStL4s2YnSWo9k50kqfVMdpKk1jPZSZJaz2QnSWo9k50kqfVMdpKk1vN3dj34+zlJao+JqtlFxG4RcXlEXBkRR446HknSZJiYZBcRqwOfAXYHtgVeExHbjjYqSdIkmJhkB+wAXJmZV2Xm74GTgb1HHJMkaQJMUrJ7HHBdo//6OkySpL4m6QGV6DIsV5ggYgGwoPbeExGXDz2q7jYBbh3RZ/djXDNjXDNjXDMztLhe+/Bmf/KqiWK8TFKyux7YqtG/JXBDc4LMXAgsnM2guomIxZk5f9RxdDKumTGumTGumRnnuEYdwzBM0mXMnwPbRMTjI2ItYH/gjBHHJEmaABNTs8vMByLib4BvAasDX8jMS0YcliRpAkxMsgPIzLOAs0YdxwBGfim1B+OaGeOaGeOaGeOaRZGZ008lSdIEm6R7dpIkrRST3cMwXfNlEfH8iPjPiHggIvYdo7jeGRGXRsTFEXF2RGw9JnEdFhG/iIgLI+JHs9VCzqDN0EXEvhGRETErT9ANUF4HR8TSWl4XRsSh4xBXneZVdRu7JCJOGnVMEfHJRjn9OiKWDTumAeOaGxHnRMQFdX/cY0zi2roeGy6OiEURseVsxDVUmenfSvxRHpL5DfAEYC3gImDbjmnmAU8HvgTsO0ZxvRB4dO1+C3DKmMS1fqP75cB/jENcdbr1gB8APwPmj0NcwMHAp2dju5phXNsAFwAb1v5NRx1Tx/RvozzgNg5ltRB4S+3eFlgyJnH9C3BQ7X4RcOJsbmfD+LNmt/Kmbb4sM5dk5sXAn8YsrnMy877a+zPKbxbHIa67Gr3r0tFowKjiqj4IfAz43SzENJO4Ztsgcb0J+Exm3gGQmbeMQUxNrwG+OuSYBo0rgfVr92Pp+O3wCOPaFji7dp/TZfzEMdmtvHFtvmymcR0CfHOoERUDxRURb42I31ASy9vHIa6I2B7YKjPPnIV4Bo6remW91HRaRGzVZfwo4noS8KSI+HFE/CwidhuDmIByeQ54PPC9Icc0aFxHAwdGxPWUJ83fNiZxXQS8sna/AlgvIjaehdiGxmS38qZtvmxEBo4rIg4E5gMfH2pE9eO6DHtIXJn5mcz8c+AI4O+HHtU0cUXEasAngcNnIZamQcrr34B5mfl04LvACUOParC41qBcytyZUos6PiI2GHFMU/YHTsvMPw4xnimDxPUa4IuZuSWwB3Bi3eZGHde7gBdExAXAC4DfAg8MOa6hMtmtvGmbLxuRgeKKiF2B9wIvz8z/Gpe4Gk4G9hlqRMV0ca0HPA1YFBFLgJ2AM2bhIZVBmse7rfHdfQ74yyHHNFBcdZrTM/MPmXk1cDkl+Y0ypin7MzuXMGGwuA4BTgXIzJ8Ca1PazBxpXJl5Q2b+dWZuTzlOkJl3Djmu4Rr1TcNJ/aOcvV5FuSQydZP3qT2m/SKz94DKtHEB21NuUG8zTuXVjAfYC1g8DnF1TL+I2XlAZZDy2rzR/QrgZ2MS127ACbV7E8ols41H/R1SGjheQv198ZiU1TeBg2v3UyhJZ6jxDRjXJsBqtfvDwDGzUWZDXe9RBzDJf5TLDr+uieO9ddgxlNoSwF9RzqLuBW4DLhmTuL4L3AxcWP/OGJO4PgVcUmM6p1/Smc24OqadlWQ3YHl9pJbXRbW8/mJM4grgE8ClwC+A/UcdU+0/Gjh2NspoBmW1LfDj+h1eCLxkTOLaF7iiTnM88KjZLLdh/NmCiiSp9bxnJ0lqPZOdJKn1THaSpNYz2UmSWs9kJ0lqvYlPdhFxdER8uXbPjYh7ImL1VfwZS+qPsLWKRcRxEfG+EX12RsQTp5nmeRFx+WzF1COGkW9/9Q0LPxplDMNWW/c/tHa/NiK+PeB8l0TEzj3G7VybAluZeFZ63lGozcNtP8ufuVlEXBYRj5pu2mmTXd3Rbo6IdRvDDo2IRQ8zzlUuM6/NzMfk7DQFBEBEfDEiPjRbnzdMo9i5MvOwzPzgys4fES+NiB9ExN31dTffj4iXr8L4fpiZT15Vy+tUD6r31L/7I+JPjf57hvW56i8zv5KZLxlw2qdm5qIhh/QQg5ysDbicg+qyDm0Me2F99dCdteWg6ZaxF3B3Zl7wcOOZicy8mfIb0wXTTTtozW4N4B0PJyiAKCa+NjmJZqPsI2KNYS6/y+ftS3kVyZcoTR5tBryf0vrKRKgH1cdk5mOA3YEbpvrrMA1gtre9toiIDYGjKI0TNN0LfAH4uwEXdRhw4ioMbSa+Arx5uokGPfh9HHhXr8ZcI+LZEfHzehbw84h4dmPcooj4cET8GLgPeEId9qGI+Ek9g/23iNg4Ir4SEXfVZcxrLONTEXFdHXd+RDyvRxzz6hnKGhHxrOYZckT8buoMJSJWi4gjI+I3EXFbRJwaERs1lvO6iLimjntvr0KJiAXAa4F3T61HHb5FRHyt1jSujoi3N+Y5OiL+JSK+XGsjv4iIJ0XEURFxS13PlzSmXxQRH4mI82r5nt4R6061HJdFxEXRuJzSo+zfEKXaf3dEXBURb67TrktpumiLRpltER011+io/UWp+R8RERcD99ay77n+XcrwweVPLTsiDq9lcWNEvKHHfFOtdHwwM4/PzDsz80+Z+f3MfFNjujfW9b0jIr4VPV5UGxF7RHnZ6N0R8duIeFeP9V3hbLoj/k0i4sz6XdweET+MVXeCsV2UtxvcGRGnRMTajRj2jPJS0mV1W3h6j3U8LiL+oWPY6RHxzto9tU/cXcviFT2W8+B+1hj24CXA2t+13KP4ZP1+76zr9LQen9N1W63jpraVIyLiJuCfZ1IWddoXR8SvahyfptFAcjQu2w5Qbg9eZo6Ideo2cUdEXEppRak5X79jQ995O5bzg9p5Ud1XX12HvynKC1lvj4gzImKLXsuoPgL8E3Brc2BmnpeZJ1KaFesrItaivPPu+41hMz3O9fuuj4jy9ow1av9bolw6ntoHzqUc2/q/hHqAZmWWALsC/wp8qA47FFhUuzcC7gBeR6kBvqb2b1zHLwKuBZ5ax69Zh10J/DnlHU6XUpql2bVO8yXgnxsxHAhsXMcdDtwErJ3LmwD6cu2eR2m9e42OdZj6zI/U/r9l+XvcHgV8Fvhqo/mee4Dn13GfoLT2vWuP8vniVLnU/tWA8yk1jLUoL0i8CnhpI97fAS9trOvVlMZW16S8C+zqxvIWUVocfxrlHW9fa6zv4yjNkO1RP/fFtX9On7J/WS33oLRmfh/wzDr9zsD106zfCtNQto8LKQ3LrjPd+vcrv7rsByjNFq1Z1+s+6ktAO+b7i/pdP77PtrsPZTt7Sl3/vwd+0hifwBNr943A82r3hr3KpDlPl/g/AhxXY18TeB4zaOewW/k3yvg8YAvK/nYZcFgd90zgFmBHyks5D6rTP6R5J8o2fd1UTHU97we2qP371c9YDXg15ex+8zruYOBHvfYzyrZ26HTlTtnuzwc2oGyDT6HRxmdHvNNtqw8AH6Xsp+vMsCw2Ae6iNIu1JvA/6vIO7bK+05XbEurxATgW+GH9nrYCfjn1nTL9saHnvD3Kp3NbfBElaT2zlsn/AX7QZ/4dgMU1rge/v45pdmWaF8pSji/3dgw7mpkd5/p916tRXpx8NKVB8TuA7Ts+72K6NO+3wjQD7IBL6go/DbgTmMOKye51wHkd8/yU5Y2bLqKjEdE67L2N/n8Evtno3wu4sE9MdwDPaBTqdMnu/wH/zvKGTS8DdmmM3xz4Q/1S3g+c3Bi3LvB7Bk92OwLXdkxzFDV513i/07Gu9wCr1/716jps0CirYxvTb1vjWZ3yGpwTOz7rWyx/w/BDyr5L/N8A3tE4gKxMsnvjoOvfr/zqsu9nxYPoLcBOXeZ7Ti2ntfus2zeBQxr9q1F2oq1rfzPZXUu5FLJ+xzI617dfsjsGOL05fiZ/3cq/UcYHNvo/BhzX2LY/2DH95cALuiwn6no+v/a/Cfhen3guBPau3QczeLLrWe6UA/KvKW+PWG2G5dO5rf6++f3PsCxeT6Ph7Fo219M92fUtN1ZMdlcBuzXGLWB5spvu2NBz3h7l0bktfh74WKP/MZTj2rwu865OSXTP6vz+OqYbJNk9B7ipY9jRzOA41++7bmxzt1OO3Ud1mf7HwOv7xTnwJZbM/CVwJnBkx6gtgGs6hl3Dii8DvI6HurnRfX+X/gfvV0S5rHVZvdywjFIbHOg1GLU6vDNwQGZOvTF8a+Dr9VLHMkoB/pFyz2eLZryZOdWI86C2plwKXNZY/nvqsqd0ruutufyhmvvr/+b9mmb5XUM5M9qkftZ+HZ/1XEry7jYvEbF7vSRwe51+Dx7+K0WanzHI+vdzW2Y235t1HyuWxYPT1f+bdxnXjOVTjThupxy4ur4ElVIW10R5yOVZA8bb9HFKjebb9VJM577ycNzU6G6WydbA4R3lvRVlO15BlqPCyZSrLwAHUO53ABARr29cAlxGOcFdmW2jZ7ln5veATwOfAW6OiIURsX63hQywrS7NzOab4wcuCx66nyfdj1PTllu/5bLisXG6faPfvINY4VicmfdQ9pNu2/t/By7O8lqhh+sOSvLqNPBxbrrvOjOXUB5EmUfZdjqtByzrF+RM7yf8T8pZTbPwbqB8iU1zKZfepuQMP+dBUe7PHQG8inI5awNKDbPbCwi7zftBytlp811M1wG7Z+YGjb+1M/O3lMtZWzWW8WjKJdReOtftOkr1vLns9TJzjwFWt5fmu6fmUs7Wbq2fdWLHZ62bmcd2iy/K47lfA/4B2KyW5VksL8tu39O9wKMb/X/WZZrmfMNY/24ur5/1yj7TXAe8uSOWdTLzJ50TZubPM3NvYFPKWeWpPZZ5Hz3KIzPvzszDM/MJlDPZd0bELjNbrRm7Dvhwxzo+OjN7vbPtq8C+9f7GjpTtgdr/OeBvKLcgNqBcRuu2n91b//faLvqWe2b+U2b+JeXy15Po8hDEANsqdN/3Bi2Lzv08WHE/69S13KZbLmV/bcbXb9/oN+8gVjgWR7kPvzErHoun7AK8IiJuinLP89nAP9Z7lzN1Rfm46Pp2+OkM8l1HxB7As4Cz6XjZdL2X90TKmyN6mlGyy8wrgVOA5gMHZwFPiogDojyc8GrKpbYzZ7LsPtajXEtfCqwREe8Hup4JNkXEVjXW12fmrztGHwd8OJbfNJ8TEXvXcacBe0bEc+uN12PoX043U669TzkPuKveVF0nIlaPiKdFRM+bzQM4MCK2rYn3GJa/afnLwF5RHr9fPSLWjnLjfssey1mLci1/KfBAROwONB+vvhnYOCIe2xh2IbBHRGwUEX9Gud/ZzzDW/yHq2fY7gffVm9vrR3nw6LkRsbBOdhxwVEQ8FSAiHhsR+3UuKyLWivITgMdm5h8o93J6/XzlQuCAul67Ue4vTC1nz4h4Yj1wTi1j2D+D+RxwWETsGMW6EfGyiOh2pk2WR8OXUl7b8q3MnDobXpeSPJbWdXkDpWbXbRlLKQfQA2s5vJFyv2VKz3KPiL+qsa5JSZq/o3sZTbetPtyy+HfgqRHx1/Vg+Xa6n8hNrXOvcut0al33Det++LbGuOn2jX7zdtN57DkJeENEbFcTyP8Czq21ok4HU+6Xblf/FgMfoL6ote5La1OuIkU9tqzVLYi6z3yXxr4wQ32/64jYhHKJ9lDKfdi9avKbsgPlUmvfmvDKPCl2DGXHAMrbkoE9KQ+O3Aa8G9gzM2/tPvuMfYtyD+DXlCr67+hxuaHDLpSN97RY/nTh1OO1nwLOoFxuupvysMqOdX0uAd5K2XBupFTR+/327PPAtvWyxDdqEtqLsgFdTamBHU+59LqyTqTcG7qJ8ibjt9dYrwP2plwKWUopl7+jx/eamXfXeU+t63UApRymxv+KcgZ7VV2fLepnX0S5L/FtyglET0Na/16fdRrlQYo3Us5qbwY+RLlvRmZ+nfIAw8kRcRelprJ7j8W9DlhSpzuM8lBUN++grN8yypO432iM24ay099DuW/9f7P+/ioivhkR71mpFe0jMxdTrrZ8mvKdXkk5kPXzVcq9mJMay7mUcu/8p5Ry/G+U+yC9vImyrd1GqaE9WFueptzXpySlOyj7822UM/rO9eq7rXYzk7Kox6f9KA+F3Eb57vqtL3Qpty4+QFmvqyn7y4OP4w+wb/Sct4ejgRPqvvqqzDwbeB+llnQj5QRk/24zZuayzLxp6o9y//OuxhWw51MuNZ5FqWHeX2Pq5bOUfWjGBviuFwKnZ+ZZNd8cAhwfEVNX3F5LOcHqy/fZjbkoP97/cmYeP+pYJKmXKD/VeFvO4g/LI2JTyk8etu+4f/sQ/hBTkvSwZeZzR/CZt1Aux07L1kwkSa3nZUxJUutZs5MktZ7JTpLUeiY7SVLrmewkSa1nspMktZ7JTpLUev8fxCYW2b9GN9IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.temp.values, bins=30, kde=False)\n",
    "plt.xlabel('Normalized temperature in Celsius. The values are divided to 41 (max)', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python36\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.atemp.values, bins=30, kde=False)\n",
    "plt.xlabel('Normalized feeling temperature in Celsius. The values are divided to 50 (max)', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python36\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.hum.values, bins=30, kde=False)\n",
    "plt.xlabel('Normalized humidity. The values are divided to 100 (max)', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python36\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.windspeed.values, bins=30, kde=False)\n",
    "plt.xlabel('Normalized wind speed. The values are divided to 67 (max)', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 两两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
